Abstract

The use of in situ hyperspectral reflectance and bio-physical measurements has been increasing in forestry. Due to limited physical accessibility in a forest environment, most of the reflectance measurements of trees are acquired at a leaf or bunch of leaves level. A few radiative transfer models are available for upscaling leaf spectra to canopy level. While these models are sophisticated, they retrieve canopy spectra based on certain assumptions. We propose ‘semi-empirical model for upscaling leaf spectra (SEMULS)’ which upscales in situ leaf spectra to canopy level based on the relationship between leaf spectra and its bio-physical parameters. The performance of the model has been quantitatively validated by comparing the upscaled canopy spectra with spectra from – CHRIS hyperspectral imagery acquired concurrently and from the PROSAIL model. Results indicate that the SEMULS retrievals are …

Abstract

Sexism, an injustice that subjects women and girls to enormous suffering, manifests in blatant as well as subtle ways. In the wake of growing documentation of experiences of sexism on the web, the automatic categorization of accounts of sexism has the potential to assist social scientists and policy makers in utilizing such data to study and counter sexism better. The existing work on sexism classification, which is different from sexism detection, has certain limitations in terms of the categories of sexism used and/or whether they can co-occur. To the best of our knowledge, this is the first work on the multi-label classification of sexism of any kind(s), and we contribute the largest dataset for sexism categorization. We develop a neural solution for this multilabel classification that can combine sentence representations obtained using models such as BERT with distributional and linguistic word embeddings using a flexible, hierarchical architecture involving recurrent components and optional convolutional ones. Further, we leverage unlabeled accounts of sexism to infuse domain-specific elements into our framework. The best proposed method outperforms several deep learning as well as traditional machine learning baselines by an appreciable margin.

Abstract

By 2050 most parts of India will be water stressed zones as most of the water resources are under heavy stress due to increasing nutrient contamination in their waters. In this scenario, studying the changes occurring in the freshwater nutrient contamination levels over a temporal scale is extremely important. This study focuses on monitoring the changes occurring in the nutrient contamination levels over a decade in a large reservoir known as Nagarjuna Sagar (NS) using remote sensing data. In this study, Landsat (5 & 8) data for the year 2005, 2009, 2015 and Sentinel (2A and 2B) data for the years 2016 and 2018 is used to study nutrient contamination in NS. The spatial spread of chlorophyll-a (chl-a) area is used as a proxy to estimate the extent of nutrient contamination in NS. In this study, only October images of NS are used as they exhibit the maximum spatial spread of Chl-a and hence help assess the contamination levels over the period 2005-2018. The analysis shows that during this period, chl-a spatial spread area has increased from 21 Km2 to 205 Km2, indicating a decrease in water quality in the reservoir. The study shows that this is accompanied by an increase in the agricultural land use area by 1000 Km2 in addition to a steep increase in the use of agricultural inputs, primarily fertilisers like urea, P and K. Thus, while the combined effect of excessive usage of fertilizers with agricultural intensification has increased crop yields, it has also contributed to damaging the freshwater resources.

Abstract

River water bodies serve as prominent water sources for various purposes ranging from drinking water, waste load allocation, irrigation, hydropower generation and ecosystem services. Human activities and natural processes require a balanced water supply and demand, while population growth, land use and climate change are the external forces which try to change the stream and river water quan-tity and quality. Water temperature is an inherent property of its quality and a con-trolling factor of health of freshwater environments. It is often considered as a driver of metabolic activity in water bodies, which influence the biological and chemical processes affecting the metabolic responses from organisms to ecosystems. The present work aims to explore sources of predictability of river water temperature (RWT) as a keen driver of hydrological and ecological processes at multiple scales. Increasing RWT in response to climate change and local-to-regional anthropogenic activities result in decreasing dissolved oxygen (DO) levels and anaerobic condi-tions in the aquatic system, thereby affecting marine life and the consequent avail-ability of food, reproduction and migration. An assessment of integrated RWT and streamflow fluctuations is proposed to evidence biological activity, chemical specia-tion, oxygen solubility and self-purification capacity of a river system and fluctua-tions of flows responsive to hydro-climate pulses. The independent and integrated contributions of air temperatures and flow fluctuations to RWT in the Missouri River near Nebraska City, USA, represent the stream responses to global raising temperatures. To quantify the contributions of multiple variations of predictor vari-ables in the air-water interfaces to RWT variability, we use a multiple regression. The performance of the model was tested along Missouri River near Nebraska City, USA, using historical series of daily river water temperature, air temperature and river discharges for the 1947–2014 period. A sensitivity analysis on river water temperature is performed, under air temperature increase of +2 °C, +4 °C and + 6 °C with a decrease of discharge of ±20%. Overall, the increase of RWT for the Missouri River is observed as about 2.76 °C under various air temperature and discharge changes when compared with the observed conditions at mean annual scale. The study results provide a comprehensive analysis of the impacts of river discharge and air temperature changes under climate change over RWT.

Abstract

This study aimed to estimate water quality–based minimum environmental flow (Eflow) of a river under the impact of different plausible scenarios. The water quality model QUAL2K was deployed to simulate levels of dissolved oxygen (DO) and biochemical oxygendemand (BOD) in a river during the dry season. Hypothetical scenarios of pollution were generated by varying BOD in contributing drains,and climate change scenarios were generated by altering air temperature over the study region. DO and BOD levels were simulated under theimpact of various scenarios. Corresponding to each scenario, minimum Eflow throughout the river stretch—that is, the headwaters flow(quantity and quality) meeting desirable river water quality standards (as proposed by the Indian Central Pollution Control Board)—was explored. Water quality charts were developed for direct and user-friendly estimation of Eflows. The proposed approach was applied to the Yamuna and Bhadra rivers in India. The results reveal the inefficiency of existing river flow conditions in maintaining permissible water quality standards. Adverse effects of pollution load, upstream diversions, and climate change are highlighted.

Abstract

Validation of precipitation data is important for hydrological modeling. Though there are many models available, rainfall prediction is difficult due to various uncertainties. This study is an attempt to compare and assess the Coordinated Regional Climate Downscaling Experiment (CORDEX) model data and India Meteorological Department (IMD) observed gridded data over Malaprabha river basin, Karnataka state, India. Both gridded data sets were downscaled at 0.25× 0.25 resolution and then processed into a 12× 115 matrix form by using QGIS (2.18. 24) and MATLAB (R2003a). These two products were compared over the period 1961-2015 to evaluate their behavior in terms of fitness by using statistical parameters such as NSE, D, R, MAE, MBE and RMSE values. Results showed that out of 16 grid points, fourteen grid points showed medium correlation ranging between 0.30 and 0.49.

Abstract

Afghanistan is the most vulnerable to climate extremes related hazards, including droughts and floods that have caused huge impact on the socio-economic development of the country. The present study analysed the observed precipitation and temperature trends for seven agro-climatic zones of Afghanistan over the period 1951 to 2006 with Asian Precipitation-Highly-Resolved Observational Data Integration towards Evaluation of Water Resources (APHRODITE). The trend analysis was performed on daily data to test the increasing or decreasing rainfall and temperature trends using Mann-Kendall trend test for each agro-climatic zone of Afghanistan. The annual total precipitation has shown an increasing trend for the zones of South, South-West, East and Central, whereas, a decreasing trend has been observed for North, North-East and West zones of Afghanistan. The trend analysis of the precipitation with gridded data sets reveals for most parts of the Afghanistan, the rainfall has been observed to be decreasing. Whereas, an increasing trend of temperatures were observed for all seven agro-climatic zones of Afghanistan.

Abstract

In recent times, our healthcare system is being challenged by many drug-resistant microorganisms and ageing-associated diseases for which we do not have any drugs or drugs with poor therapeutic profile. With pharmaceutical technological advancements, increasing computational power and growth of related biomedical fields, there have been dramatic increase in the number of drugs approved in general, but still way behind in drug discovery for certain class of diseases. Now, we have access to bigger genomics database, better biophysical methods, and knowledge about chemical space with which we should be able to easily explore and predict synthetically feasible compounds for the lead optimization process. In this chapter, we discuss the limitations and highlights of currently available computational methods used for protein–ligand binding affinities estimation and this includes force-field, ab initio electronic structure theory and machine learning approaches. Since the electronic structure-based approach cannot be applied to systems of larger length scale, the free energy methods based on this employ certain approximations, and these have been discussed in detail in this chapter. Recently, the methods based on electronic structure theory and machine learning approaches also are successfully being used to compute protein–ligand binding affinities and other pharmacokinetic and pharmacodynamic properties and so have greater potential to take forward computer-aided drug discovery to newer heights

Abstract

Hydroxymethylbilane synthase (HMBS), the third enzyme in the heme biosynthesis pathway, catalyzes the formation of 1-hydroxymethylbilane (HMB) by a stepwise polymerization of four molecules of porphobilinogen (PBG) using the dipyrromethane (DPM) cofactor. The mechanism by which HMBS polymerizes four units of PBG has not been elucidated to date. In vitro and in silico studies on HMBS have suggested certain residues with catalytic importance, but their specific role in the catalysis is unclear. To understand the catalytic mechanism of HMBS, quantum mechanical (QM) calculations were performed on model systems obtained from the active site of the human HMBS enzyme. The addition of one molecule of PBG to the DPM cofactor is carried out in four steps: (1) protonation of the substrate, PBG; (2) deamination of PBG; (3) electrophilic addition of the deaminated substrate to the terminal pyrrole ring of the enzyme-bound DPM cofactor and (4) deprotonation of the carbon atom at the a-position of the second ring of DPM. Based on the energy profiles from the QM calculations on cluster models, R26 is proposed to be the best suitable proton donor to the PBG moiety, which aids in the deamination of the substrate. During the electrophilic addition step, the intermediate formed is stabilized by the carboxylate side chain of the D99 residue. In the final deprotonation step, an extra proton from the second ring of DPM is transferred to R26 via the carboxylate side chain of D99, thus completing one cycle of the catalytic mechanism. The residues in the cluster model seem to play an important role in obtaining accurate energy barriers. All the stationary points along the reaction pathway have been characterized using QM calculations. The rate limiting step for the complete mechanism is found to be the deamination of the PBG moiety. The results of this study provide a detailed understanding of the catalytic mechanism and would help design future studies aimed at modulating the activity of HMBS

Abstract

Motivated by the persistent anomalies in the semileptonic B-meson decays, we investigate the competency of LHC data to constrain the RDðÞ -favored parameter space in a charge −1=3 scalar leptoquark (S1) model. We consider some scenarios with one large free coupling to accommodate the RDðÞ anomalies. As a result, some of them dominantly yield nonresonant ττ and τν events at the LHC through the t-channel S1 exchange. So far, no experiment has searched for leptoquarks using these signatures and the relevant resonant leptoquark searches are yet to put any strong exclusion limit on the parameter space. We recast the latest ττ and τν resonance search data to obtain new exclusion limits. The nonresonant processes strongly interfere (destructively in our case) with the Standard Model background and play the determining role in setting the exclusion limits. To obtain precise limits, we include non-negligible effects coming from the subdominant (resonant) pair and inclusive single leptoquark productions systematically in our analysis. To deal with large destructive interference, we make use of the transverse mass distributions from the experiments in our statistical analysis. In addition, we also recast the relevant direct search results to obtain the most stringent collider bounds on these scenarios to date. These are independent bounds and are competitive to other known bounds. Finally, we indicate how one can adopt these bounds to a wide class of models with S1 that are proposed to accommodate the RDðÞ anomalies.